Data Scientist (Geo) - Join our mission here in Singapore!

What we do

Grab is Southeast Asia's leading super-app that provides everyday services that matter the most to consumers. Through its open platform strategy, Grab works with partners to provide safe, accessible and affordable transport, food, package, grocery delivery, mobile payments and financial services to millions of Southeast Asians.

Why we do

Mission
1. Trust that you will have a safe ride
Travel with confidence knowing that Grab’s top priority is your safety. From driver safety training and vehicle safety checks, to personal accident insurance coverage for all our drivers and passengers and government partnerships to promote safety, you know we have your back.

2. Take the transport option that fits your need
We put freedom in your hands. The most transport options, at every price point, with comfort, speed and affordability – you can have it all at the touch of a button.

3.Let us take care of you
We believe that a sustainable business is one that improves the lives of the people it touches – passengers, drivers, employees, governments and society at large.

How we do

Life at Grab is all about positive disruption – and yes, crazy days are part of that package too. Still, that’s never stopped a Grabber from having fun. In fact, it’s what keeps us motivated to shake things up further.

Life as a Grabber means succeeding in a culture of passion and innovation. We are hungry to make a difference, and recognise that good decisions often come from the heart. We are humbled by our communities, and are proud to serve them with honour. We come from all over the world, united by a common goal to make life better everyday for our users.

If you share our mission of Driving Southeast Forward, apply to be part of the team today!

As a new team member

Grab’s Data Science Department works on some of the most challenging and fascinating problems in transport, logistics, economics, and the space around. We apply deep learning, geospatial data mining, simulation, forecasting, scheduling, optimization, and many other advanced techniques on our huge datasets to push our business metrics to their bounds, directly and indirectly. We foster a culture where we enjoy raising the bar constantly for ourselves and others, and that strongly supports the freedom to explore and innovate.
Sample of problems the Data Science Department solve - Intelligent allocation, machine/deep learning - based predictions (all sorts!), Dynamic pricing, Supply/demand forecasting and positioning, Incentives and promotions optimization, Carpooling matching, Shuttle and on-demand bus routing and scheduling, Multi-modal transport, Geospatial data mining, etc.
The Machine Learning team at Grab provides an opportunity to apply supervised as well as unsupervised learning on large scale data. We build algorithms and models to match passenger and driver, we predict time of arrival, we predict when a driver will churn (i.e. stop driving for Grab) etc. We explore models ranging from simple linear ones to ensembles to deep learning. If you enjoy predictions and algorithms, let's have a chat!

Get to know the Role:
• Build, validate, test, and deploy machine learning models (e.g. predictive, forecasting, clustering) using proven and experimental techniques. Deploy an online learning model where applicable
• Define hypotheses, develop and execute necessary tests, experiments, and analyses to prove or disprove them
• Translate data speak to human speak by effectively conceptualizing analysis to team members and business stakeholders

The must haves:
• Ph.D. graduate, or Masters(with at least 3 years of experience), in Computer Science, Electrical/Computer Engineering , Operations Research or Mathematics/Statistics
• Understanding of machine learning, deep learning, data mining, algorithmic foundations of optimization
• Experience with machine learning framework (scikit-learn, Spark MLlib etc)
• Proficient in one or more of the following programming languages: Python, R, Scala.
• Experience in building ML models at scale, using real-time big data pipelines on platforms such as Spark/MapReduce
• Familiar with noSQL, postGIS, stream processing and distributed computing platforms
• Self-motivated, independent learner, and willing to share knowledge with team members
• Detail-oriented and efficient time manager in a dynamic and dynamic working environment

IF YOU ARE INTERESTED, PLEASE CLICK "WANT TO VISIT". ONLY SHORTLISTED CANDIDATES WILL BE CONTACTED.